Early dementia biomarker based on the response to sensory stimulation during non-rapid eye movement (nrem) sleep

ABSTRACT

An apparatus and method for early detection of dementia overcomes the shortcomings of conventional systems and methods for detecting dementia. This is achieved by providing an apparatus and method for employing an early dementia biomarker based on the response to sensory stimulation during sleep, such as during non-rapid eye movement (NREM) sleep.

CROSS-REFERENCE TO PRIOR APPLICATIONS

This application claims the benefit of U.S. Provisional Application No. 62/990,110, filed on 16 Mar. 2020. This application is hereby incorporated by reference herein

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention pertains to early detection of dementia, and, in particular, to an apparatus and method for employing an early dementia biomarker based on the response to sensory stimulation during sleep, for example during non-rapid eye movement (NREM) sleep.

2. Description of the Related Art

Recent evidence suggests that early diagnosis of dementia significantly enhances the favorable prognosis of the treatment. However, current biomarkers, especially behavioral or cognitive ones, lack sensitivity to detect early characteristic brain changes including amyloid plaque accumulations and neurofibrillary, or tau, tangles that result in loss of neurons and their connections.

By 2030, 20% of the US population will be of retirement age, and this proportion will increase to 25% by year 2060. In order to ensure independent living and quality of life to this aging population, it is essential to promote successful cognitive aging and ensure early detection of abnormal cognitive decline. Alzheimer's Disease (AD) has an extensive preclinical stage believed to last for a few decades before the appearance of clinically discernable symptoms. R. A. Sperling et al., “Toward defining the preclinical stages of Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease,” Alzheimer's Dement., vol. 7, no. 3, pp. 280-92, 2011. Indeed, post-mortem brain examination of individuals without clinically diagnosed cognitive impairment or with Mild Cognitive Impairment (MCI) diagnostic often reveals similar pathological features as those with confirmed AD. J. L. Price and J. C. Morris, “Tangles and plaques in nondemented aging and ‘preclinical’ Alzheimer's disease,” Ann. Neurol., vol. 45, no. 3, pp. 358-68, 1999; J. C. Morris et al., “Mild cognitive impairment represents early-stage Alzheimer disease,” Arch. Neurol., vol. 58, no. 3, pp. 397-405, 2001.

These observations have motivated the quest of a biomarker for identifying individuals in the earliest preclinical stages of AD in order to facilitate early intervention and to delay or even prevent the onset of clinical symptoms. Furthermore, biomarkers for AD progression may also have clinical utility for tracking the efficacy of potential disease-modifying therapies. S. E. Counts, M. D. Ikonomovic, N. Mercado, I. E. Vega, and E. J. Mufson, “Biomarkers for the Early Detection and Progression of Alzheimer's Disease,” Neurotherapeutics, vol. 14, no. 2017, pp. 35-53, 2016.

According to a model for AD progression biomarkers (C. R. Jack et al., “Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade,” The Lancet Neurology, vol. 9, no. 1. Lancet Publishing Group, pp. 119-28, 2010), and as illustrated in FIG. 1, amyloid (Aβ) changes detectable in cerebrospinal fluid (CSF), Aβ42 assay, or Positron Emission Tomography (PET) amyloid imaging 4 precede markers for synaptic dysfunction 8 such as synaptic loss (J. Jackson et al., “Targeting the synapse in Alzheimer's disease,” Front. Neurosci., vol. 13, no. JUL, pp. 1-8, 2019) and neurotransmitter deficits. The latter are identified through fluorodeoxyglucose (FDG)-PET and functional Magnetic Resonance Imaging (fMRI). These alterations are closely associated with increased CSF tau 12, which serves as a surrogate for neuronal injury. A leftmost (i.e., temporally earliest) portion 14 of synaptic dysfunction 8 line indicates that synaptic dysfunction may be detectable in apolipoprotein E (ApoE) ε4 carriers prior to detectable Aβ deposition. Brain atrophy on structural MM 16 and subtle decline in cognitive function 20 mark the transition from preclinical AD to mild cognitive impairment (MCI).

Increasing evidence supports the concept of considering abnormalities in the macro and micro structure of sleep as markers for early detection of AD pathology and to estimate the risk of future cognitive impairment. B. P. Lucey et al., “Reduced non-rapid eye movement sleep is associated with tau pathology in early Alzheimer's disease,” Sci. Transl. Med., vol. 11, no. 474, 2019. Lucey et al. suggest that changes in the electroencephalogram (EEG) power in the 1-2 Hz band during NREM sleep may enable the discrimination of tau pathology and cognitive impairment either before or during the early stages of symptomatic AD.

According to the model, as in FIG. 2, proposed by Mander et al. (B. A. Mander, J. Winer, W. J. Jagust, and M. P. Walker, “Sleep: A Novel Mechanistic Pathway, Biomarker, and Treatment Target in the Pathology of Alzheimer's Disease?,” Trends Neurosci., vol. 39, no. 8, pp. 552-66, 2016) for the process regulating the reciprocity between NREM sleep and wake in regard to Aβ burden, during wake:

-   -   glymphatic amyloid clearance is low (L. Xie et al., “Sleep         drives metabolite clearance from the adult brain,” Science, vol.         342, no. 6156, pp. 373-77, 2013);     -   neurometabolic and neuronal spiking activity is high (M.         Buchsbaum et al., “Regional Cerebral Glucose Metabolic Rate in         Human Sleep Assessed by Positron Emission Tomography,” Life         Sci., vol. 45, no. 15, pp. 1349-56, 1989); and     -   oxidative stress is high (G. Villafuerte, A. Miguel-Puga, E.         Murillo Rodriguez, S. Machado, E. Manjarrez, and O.         Arias-Carrión, “Sleep deprivation and oxidative stress in animal         models: A systematic review,” Oxid. Med. Cell. Longev., vol.         2015, pp. 1-15, 2015).

Because of these factors, wake promotes high Aβ burden. Aβ in turn promotes:

-   -   higher oxidative stress (S. M. Yatin, S. Varadarajan, C. D.         Link, and D. A. Butterfield, “In vitro and in vivo oxidative         stress associated with Alzheimer's amyloid β-peptide (1-42),”         Neurobiol. Aging, vol. 20, no. 3, pp. 325-30, 1999);     -   neuronal hyperexcitability (M. Tabuchi et al., “Sleep interacts         with aβ to modulate intrinsic neuronal excitability,” Curr.         Biol., vol. 25, no. 6, pp. 702-12, 2015); and     -   reduced glymphatic clearance, presumably by causing cerebral         amyloid angiopathy (R. O. Weller, S. D. Preston, M. Subash,         and R. O. Carare, “Cerebral amyloid angiopathy in the aetiology         and immunotherapy of Alzheimer disease,” Alzheimers. Res. Ther.,         vol. 1, no. 2, pp. 1-13, 2009) (i.e. amyloid proteins build up         on the walls of the arteries in the brain) and NREM sleep         disturbance.

These factors constitute a positive feedback loop where Aβ appears to promote its own accumulation (B. A. Mander et al., “Beta-amyloid disrupts human NREM slow waves and related hippocampus-dependent memory consolidation,” Nat. Neurosci., vol. 18, no. 7, pp. 1-10, 2015).

During NREM sleep:

-   -   glymphatic Aβ clearance is high (“Sleep drives metabolite         clearance from the adult brain,” supra);     -   neurometabolic rate is low (A. R. Braun et al., “Regional         cerebral blood flow throughout the sleep-wake cycle. An H215O         PET study,” Brain, vol. 120, no. 7, pp. 1173-97, 1997); and     -   active cellular processes promote cellular restitution that         reduces oxidative damage (C. A. Everson, C. J. Henchen, A.         Szabo, and N. Hogg, “Cell Injury and Repair Resulting from Sleep         Loss and Sleep Recovery in Laboratory Rats,” Sleep, vol. 37, no.         12, pp. 1929-40, 2014).

NREM sleep balances both Aβ accumulation and the negative consequences of Aβ accumulation under conditions of healthy sleep.

Under pathological conditions, however, Aβ burden may actively disrupt NREM sleep. “Beta-amyloid disrupts human NREM slow waves and related hippocampus-dependent memory consolidation,” supra. This disruption, alongside reduced Aβ clearance due to cerebral amyloid angiopathy (“Cerebral amyloid angiopathy in the aetiology and immunotherapy of Alzheimer disease,” supra), creates an environment where NREM sleep can no longer successfully suppress Aβ accumulation. This exacerbates the vicious positive feedback that triggers higher Aβ aggregation and accelerates AD progression.

Methods to detect early Aβ burden and/or tau pathology are known but they rely on brain imaging or CSF samples. These are burdensome and costly for patients, and this limits their practicality and frequency of application. Approaches based on the analysis of electrical brain activity (i.e. from an EEG) during sleep are known but they are observational in nature, i.e. they rely on the analysis of EEG power during NREM sleep. As such, improvements in the early detection of dementia would be desirable.

SUMMARY OF THE INVENTION

Accordingly, it is an object of the present invention to provide an improved apparatus and method for early detection of dementia that overcomes the shortcomings of conventional systems and methods for detecting dementia. This object is achieved according to one embodiment of the present invention by providing an apparatus and method for employing an early dementia biomarker based on the response to sensory stimulation during sleep, such as during non-rapid eye movement (NREM) sleep.

Given the early pathophysiology of AD which adversely affects synapses, the detection accuracy can be improved by probing brain cortical networks in the brain through sensory stimulation during sleep and characterizing the response to the stimulation. Indeed, it is known that sensory stimulation (particularly auditory or vibrational) during NREM increases (or enhances) the amplitude of sleep slow waves, provided the integrity of synapses in the brain. G. Tononi and C. Cirelli, “Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration,” Neuron, vol. 81, no. 1, pp. 12-34, January 2014. In fact, the susceptibility of enhancing the amplitude of slow waves through auditory stimulation is likely to be reduced if fewer synapses are engaged. J. Schneider, P. A. Lewis, D. Koester, J. Born, H.-V. V. Ngo, “Susceptibility to auditory closed-loop stimulation of sleep slow oscillations changes with age,” Sleep, zsaa111, 2019.

The disclosed and claimed concept thus advantageously provides a sensory-stimulation/response-evaluation approach during sleep to develop a biomarker for the early detection of synaptic dysfunction in AD pathology.

Accordingly, aspects of the disclosed and claimed concept are provided by an improved method of detecting cognitive decline in a person, the general nature of which can be stated as including monitoring at least a portion of an electroencephalogram (EEG) signal of the person during at least one sleep cycle, applying to the person a sensory stimulation during the at least one sleep cycle, quantifying a property of the EEG signal in response to the sensory stimulation, correlating the property with a synaptic network depopulation with the use of a model, and generating an output that is representative of the synaptic network depopulation.

Other aspects of the disclosed and claimed concept are provided by an improved apparatus structured to detecting cognitive decline in a person, the general nature of which can be stated as including a processor apparatus, the general nature of which can be stated as including a processor and a storage, an input apparatus structured to provide input signals to the processor apparatus, an output apparatus structured to receive output signals from the processor apparatus, and the storage having stored therein a number of instructions which, when executed on the processor, cause the apparatus to perform a number of operations, the general nature of which can be stated as including monitoring at least a portion of an electroencephalogram (EEG) signal of the person during at least one sleep cycle, applying to the person a sensory stimulation during the at least one sleep cycle, quantifying a property of the EEG signal in response to the sensory stimulation, correlating the property with a synaptic network depopulation with the use of a model, and generating an output that is representative of the synaptic network depopulation.

These and other objects, features, and characteristics of the present invention, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic biomarker overview;

FIG. 2 is a schematic depiction of a model of the process regulating the reciprocity between NREM sleep and wake in regard to Aβ burden;

FIG. 3 is a depiction of a response to stimulation during sleep as an early AD biomarker;

FIG. 4 is a chart depicting changes over time in the EEG amplitude in response to sensory stimulation during sleep; and

FIG. 5 is a box plot depicting maximum EEG amplitude of the response to auditory stimulation versus age group and comparison with MCI patients;

FIG. 6 depicts an apparatus in accordance with the disclosed and claimed concept; and

FIG. 7 depicts an improved method in accordance with the disclosed and claimed concept.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

As used herein, the singular form of “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise. As used herein, the statement that two or more parts or components are “coupled” shall mean that the parts are joined or operate together either directly or indirectly, i.e., through one or more intermediate parts or components, so long as a link occurs. As used herein, “directly coupled” means that two elements are directly in contact with each other. As used herein, “fixedly coupled” or “fixed” means that two components are coupled so as to move as one while maintaining a constant orientation relative to each other.

As used herein, the word “unitary” means a component is created as a single piece or unit. That is, a component that includes pieces that are created separately and then coupled together as a unit is not a “unitary” component or body. As employed herein, the statement that two or more parts or components “engage” one another shall mean that the parts exert a force against one another either directly or through one or more intermediate parts or components. As employed herein, the term “number” shall mean one or an integer greater than one (i.e., a plurality).

Directional phrases used herein, such as, for example and without limitation, top, bottom, left, right, upper, lower, front, back, and derivatives thereof, relate to the orientation of the elements shown in the drawings and are not limiting upon the claims unless expressly recited therein.

Aspects of the disclosed and claimed concept are shown in FIG. 3. The patient's sleep EEG 22 is measured and NREM sleep is predicted in real time. E. Bresch, U. Grossekathofer, and G. Garcia-Molina, “Recurrent deep neural networks for real-time sleep stage classification from single channel EEG,” Front. Comput. Neurosci., vol. 12, no. 2018, pp. 1-12, 2018; G. Garcia-molina et al., “Hybrid in-phase and continuous auditory stimulation significantly enhances slow wave activity during sleep,” in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019, pp. 4052-55. On detection of NREM sleep, sensory stimulation is delivered with intensity and timing that are optimized according to properties in the sleep EEG that inform on sleep depth, e.g. ratio between the delta (0.5-4 Hz) and beta (15-30 Hz) power, and the propensity of sleep disturbance, e.g. the relative EEG power in the alpha (8-12 Hz) band. G. Garcia-Molina et al., “Closed-loop system to enhance slow-wave activity,” J. Neural Eng., vol. 15, no. 6, pp. 1-11, 2018.

One of the earliest manifestations of dementia pathology is the loss of cortical synapses. “Targeting the synapse in Alzheimer's disease,” supra. It is known that the EEG amplitude of the response to sensory stimulation during sleep directly depends on the number of synapses. “Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration,” supra. Therefore, the attenuation of the EEG amplitude of the response to the sensory stimulation advantageously directly correlates with synaptic network depopulation which may be the result of early AD pathology. This is especially useful for tracking the progression of synaptic network depopulation because the method can be applied over several nights. It is noted that synaptic network depopulation can encompass synaptic loss, destruction of neurons, amyloid accumulation, and/or any of a variety of other conditions. While the amplitude of the response decreases with age (“Susceptibility to auditory closed-loop stimulation of sleep slow oscillations changes with age,” supra), the decrease due to early amyloid accumulation is expected to be more drastic. This concept is illustrated in FIG. 4.

In the disclosed and claimed concept, auditory stimulation (in the form of a number of brief tones ˜50 millisecond-long) is delivered during NREM sleep using a consumer device, an example of which is the Philips SmartSleep™ deep-sleep headband. As employed herein, the expression “a number of” and variations thereof shall refer broadly to any non-zero quantity, including a quantity of one. Numerous references report that auditory stimulation delivered in-phase with the up-state of a slow-wave enhances the slow-wave amplitude. G. Garcia-Molina et al., “Closed-loop system to enhance slow-wave activity,” J. Neural Eng., vol. 15, no. 6, pp. 1-11, 2018; H. V. Ngo, A. Miedema, I. Faude, T. Martinetz, M. Molle, and J. Born, “Driving Sleep Slow Oscillations by Auditory Closed-Loop Stimulation-A Self-Limiting Process,” J. Neurosci., vol. 35, no. 17, pp. 6630-38, 2015; N. A. Papalambros et al., “Acoustic enhancement of sleep slow oscillations and concomitant memory improvement in older adults,” Front. Hum. Neurosci., vol. 11, no. March, pp. 1-14, 2017; N. A. Papalambros, S. Weintraub, T. Chen, G. Santostasi, K. A. Paller, and P. C. Zee, “Acoustic enhancement of sleep slow oscillations in mild cognitive impairment,” pp. 1-42, 2019. This effect can be observed in young subjects (24.2±0.9 years) (H. V. Ngo, T. Martinetz, J. Born, and M. Molle, “Auditory Closed-Loop Stimulation of the Sleep Slow Oscillation Enhances Memory,” Neuron, vol. 78, no. May, pp. 1-9, 2013), older subjects (60 to 84 year old) (“Acoustic enhancement of sleep slow oscillations and concomitant memory improvement in older adults,” supra), and even in patients with mild cognitive impairment (“Acoustic enhancement of sleep slow oscillations in mild cognitive impairment,” supra).

Using data from various pilot studies with auditory stimulation during sleep, the amplitude of the EEG response depending on age was estimated, as in FIG. 5. In order to unambiguously characterize the response, the maximum amplitude across all delivered tones is represented in FIG. 5. Similar data from three subjects affected by mild cognitive impairment (MCI) is also shown in FIG. 5. While the response amplitude generally decrease with age, the response in MCI patients is clearly lower.

Reference data to screen for abnormally low EEG amplitude can be obtained from a database formed from the aforementioned data or can be alternatively built from data collected with a consumer device or other device, such as the exemplary aforementioned SmartSleep deep-sleep headband. In some embodiments the notion of abnormally low EEG amplitude corresponds to one standard deviation lower than the age-group average. In certain embodiments, the type of sensory stimulation is auditory, vibratory, olfactory, or a combination thereof. In other embodiments, the EEG amplitude response is estimated over several sleep sessions (for instance five).

The rapid aging of the population worldwide makes it imperative to leverage sleep technology for two purposes: 1) as an early biomarker of abnormal cognitive decline; and 2) to prevent or delay abnormal cognitive decline. The disclosed and claimed concept advantageously addresses at least the first purpose by modeling synaptic network depopulation through the quantification of the EEG amplitude in response to sensory stimulation during NREM sleep. In addition to serving as an early AD biomarker, the disclosed and claimed concept also has advantageous clinical utility for tracking the efficacy of potential disease-modifying therapies.

An improved apparatus 24 in accordance with the disclosed and claimed concept is depicted generally in FIG. 6. Apparatus 24 can be said to include a processor apparatus 28 that includes a processor 32 and a storage 36. Processor 32 can be any of a wide variety of processors, such as microprocessors and the like. Storage 36 can be any of a wide variety of storage devices and serves as a non-transitory storage medium. Storage 36 has stored therein a number of routines 52 that are executable as instructions on processor 32 and which cause apparatus 24 to perform certain operations such as are set forth elsewhere herein.

Storage 36 further has a model 56 stored therein wherein cognitive decline is correlated with a property from an EEG signal. In this regard, the EEG signal 22 can have any of a wide variety of properties such as amplitudes, slopes, power spectral densities, and the like without limitation, and during the sleep cycle any such desired property of the EEG signal 22 can be quantified. For instance, in the frequency domain of the EEG signal, the property may be a power spectral density in a band of approximately 0.5-4.0 Hz in the EEG signal.

The value or magnitude of the property obtained from the EEG signal 22 is then submitted to the model 56, which returns a value for a synaptic network depopulation that is correlated with the value of the property. Model 56 can be generated in any of a wide variety of fashions and can be, by way of example, a generic population-based model or can be a personalized (longitudinal) model that is constructed using multiple sleep EEG recordings that may be acquired through a time interval that may span a number of months or a number of years, by way of example.

Apparatus 24 further includes an input apparatus 44 that provides input signals to the processor 32. For example, input apparatus 44 can include the SmartSleep deep-sleep headband or other such device that detects EEG signal components, and the input signals that are provided to processor 32 thus can include an EEG signal for the person as well as other input signals.

Apparatus 24 further includes an output apparatus 48 that receives output signals from processor 32 and that generates an output that can be in the form of a visual output, such as one that is depicted on a visual display, or that can include raw data and other output such as audible outputs and the like. Furthermore, output apparatus 48 applies the aforementioned sensory stimulation to the person during the sleep cycle. For instance, if the sensory stimulation is audible in nature, it can be output by a loudspeaker that is situated in proximity to the ears of the person. By way of example, the SmartSleep deep-sleep headband includes such a loudspeaker which would be considered to be a component of output apparatus 48 whereas the sensors of the SmartSleep deep-sleep headband that detect the components of EEG signal 22 would be considered to be part of input apparatus 44.

Input apparatus 44, or at least a portion thereof, is thus connected with the person and inputs to processor 32 the person's EEG signal 22. Once the desired level of sleep is detected, routines 52 perform a method 100 such as is set forth generally in FIG. 7. As part of the execution of method 100, output apparatus 48 will apply to the person a sensory stimulation during the sleep cycle in which the person's EEG signal 22 is being monitored. It thus can be understood that portions of input apparatus 44 and portions of output apparatus 48 will be situated on or in proximity to the person in order to detect from the person and input into processor 32 the EEG signal 22 and in order to provide the sensory stimulation to the person.

An improved method 100 in accordance with the disclosed and claimed concept is depicted generally in FIG. 7. The method 100 can be said to include, as at 110, monitoring the EEG signal 22 during a sleep cycle. For better results, such monitoring can occur over a plurality of sleep cycles and, even more desirably, is performed on an ongoing basis with portions of the EEG signal 22 being saved in storage 36 and optionally being used to supplement model 56.

Processing continues, as at 120, wherein, once a desired level of sleep is detected in the person, routines 52 apply to the person a sensory stimulation during the sleep cycle. As the EEG signal 22 is monitored by routines 52, the routines 52 will furthermore evaluate the EEG signal that occurs in response to the application of the sensory stimulation and will quantify a property of the EEG signal 22. In this regard, the property could be, by way of example, and amplitude of the EEG signal 22 that occurs with application of the sensory stimulation or may be another property such as a slope or other desired property of the EEG signal 22.

Routines 52 then correlate, as at 140, the quantified property of the EEG signal 22 with a synaptic network depopulation through the use of model 56. In this regard, model 56 might include a set of data in which a given value for the property of the EEG signal 22, such as an amplitude or slope of the EEG signal 22, corresponds with a given synaptic network depopulation that is attributed to the person. Processing then continues, as at 150 where the routines 52 generate an output that is representative of the synaptic network depopulation of the person and that is output by output apparatus 48. As noted elsewhere herein, such output can be in the form of a visual output, can be in the form of raw data that is input into another device, or can be in any of a variety of other forms.

In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word “comprising” or “including” does not exclude the presence of elements or steps other than those listed in a claim. In a device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The word “a” or “an” preceding an element does not exclude the presence of a plurality of such elements. In any device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The mere fact that certain elements are recited in mutually different dependent claims does not indicate that these elements cannot be used in combination.

Although the invention has been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred embodiments, it is to be understood that such detail is solely for that purpose and that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the spirit and scope of the appended claims. For example, it is to be understood that the present invention contemplates that, to the extent possible, one or more features of any embodiment can be combined with one or more features of any other embodiment. 

What is claimed is:
 1. A method of detecting cognitive decline in a person, comprising: monitoring at least a portion of an electroencephalogram (EEG) signal of the person during at least a portion of at least one sleep cycle; applying to the person a sensory stimulation during the at least one sleep cycle; quantifying a property of the EEG signal in response to the sensory stimulation; correlating the property with a synaptic network depopulation with the use of a model; and generating an output that is representative of the network depopulation.
 2. The method of claim 1 wherein the property is an amplitude of a portion of the EEG signal.
 3. The method of claim 1 wherein the property is a slope of a portion of the EEG signal.
 4. The method of claim 1 wherein the correlating of the property with the synaptic network depopulation with the use of a model comprises employing a generic population-based model.
 5. The method of claim 1 wherein the correlating of the property with the synaptic network depopulation with the use of a model comprises employing a personalized model that is constructed using multiple sleep EEG recordings acquired through a time interval.
 6. The method of claim 5 wherein the employing of the personalized model comprises employing as the personalized model a cumulative model spanning a number of months.
 7. The method of claim 1 wherein the property is a power spectral density of a portion of the EEG signal.
 8. The method of claim 1 wherein the property is a power spectral density in a band of approximately 0.5-4.0 Hz in a frequency domain of the EEG signal.
 9. An apparatus structured to detecting cognitive decline in a person, comprising: a processor apparatus comprising a processor and a storage; an input apparatus structured to provide input signals to the processor apparatus; an output apparatus structured to receive output signals from the processor apparatus; and the storage having stored therein a number of instructions which, when executed on the processor, cause the apparatus to perform a number of operations comprising: applying to the person a sensory stimulation during the at least one sleep cycle; quantifying a property of the EEG signal in response to the sensory stimulation; correlating the property with a synaptic network depopulation with the use of a model; and generating an output that is representative of the synaptic network depopulation.
 10. The apparatus of claim 9 wherein the property is an amplitude of a portion of the EEG signal.
 11. The apparatus of claim 9 wherein the property is a slope of a portion of the EEG signal.
 12. The apparatus of claim 9 wherein the correlating of the property with the synaptic network depopulation with the use of a model comprises employing a generic population-based model.
 13. The apparatus of claim 9 wherein the correlating of the property with the synaptic network depopulation with the use of a model comprises employing a personalized model that is constructed using multiple sleep EEG recordings acquired through a time interval.
 14. The apparatus of claim 13 wherein the employing of the personalized model comprises employing as the personalized model a cumulative model spanning a number of months.
 15. The apparatus of claim 9 wherein the property is a power spectral density of a portion of the EEG signal.
 16. The apparatus of claim 9 wherein the property is a power spectral density in a band of approximately 0.5-4.0 Hz in a frequency domain of the EEG signal. 